This happens every time something goes wrong on the stock market — every time there’s a flash crash, or a high-frequency trading firm blows up, or the Nasdaq is forced to go dark for three hours. A bunch of editors who don’t really know anything about HFT ask for stories about it, and they all want the same thing: a tale of how a small group of high-speed trading shops, armed with state-of-the-art computers, are using their artificial information advantage, and their lightning-fast speed, to extract enormous rents from the little guy.
The result is a spate of stories like Rob Curran’s latest piece for Fortune, which appears under the headline “Make $377,000 trading Apple in one day”. Of course, there are lots of ways to do that: one way would be to buy about 77,000 shares of Apple, for $37.7 million, and then watch them rise by 1%. But Curran reckons he’s found a better way — indeed, an easy profit which involves no risk at all. What’s more, this method is particularly evil, since apparently all of the profits that it generates are coming straight out of your pocket.
Curran’s story is based in large part on a “study” by Berkeley professor Terrence Hendershott. This study is never named, or quoted, or linked to, and I can’t find it on Hendershott’s web page, so I’m not going to blame Hendershott for any of the content of Curran’s article. Specifically, for instance, Curran’s sub-hed says that “A Berkeley professor finds out just how much a certain type of high frequency trading costs the average investor”. I suspect that Hendershott’s study actually purports to do no such thing*, and that “average investors” aren’t even mentioned in it. I say this because Hendershott is a smart guy, and I can’t believe that this kind of thing fairly summarizes any of his work:
It’s well known that some high-frequency computer geeks at firms like Getco LLC take advantage of latency, just as it’s well known that some Blackjack-playing computer geeks count cards in Las Vegas casinos. But it’s never been clear how much this type of trading costs the little guy on Wall Street.
Terrence Hendershott, a professor at the Haas business school at the University of California at Berkeley, wanted to find out. He was recently given access to high-speed trading technology by tech firm Redline Trading Solutions. His test exposes the power of latency arbitrage the way Ben Mezrich’s Bringing Down the House exposed the power of card counting.
According to his study, in one day (May 9), playing one stock (Apple), Hendershott walked away with almost $377,000 in theoretical profits by picking off quotes on various exchanges that were fractions of a second out of date. Extrapolate that number to reflect the thousands of stocks trading electronically in the U.S., and it’s clear that high-frequency traders are making billions of dollars a year on a simple quirk in the electronic stock market.
One way or another, that money is coming out of your retirement account. Think of it like the old movie The Sting. High-speed traders already know who has won the horse race when your mutual fund manager lays his bet. You’re guaranteed to come out a loser. You’re losing in small increments, but every mickle makes a muckle — especially in a tough market.
This is deeply confused. For one thing, there’s much more to HFT than simple latency arbitrage of the kind that Curran is describing here. And in any case, this kind of strategy just doesn’t work. To see why, just do the extrapolation Curran’s asking you to do. If high-frequency traders are making $377,000 per stock per day, then that would add up — multiply by 5,000 stocks, and by 250 days per year — to total profits of almost $500 billion per year, or about 3% of America’s GDP. And that doesn’t even include the extra profits made by high-frequency trading in other asset classes, like foreign stocks, or currencies, or interest rates.
Curran’s number, in other words, doesn’t pass the smell test.
Note that Hendershott’s one-day profit was “theoretical” — Curran never asks the question of whether Redline in practice makes anything like that of money, or if they don’t, why they don’t.
In the real world, it should probably go without saying, hundreds of billions of dollars in annual risk-free profits aren’t just sitting on trees, waiting to be plucked. The idea behind latency arbitrage is simple: you’re essentially trying to buy or sell at yesterday’s prices, in the knowledge of where the price is today. (Except, we’re talking about a time lag measured in milliseconds, rather than days.) If you were to actually enter the market with a simple latency-arbitrage algorithm like this one, however, you would almost certainly lose your shirt in no time: a thousand other algobots would immediately recognize your pattern, and pick you off systematically.
But Curran seems to be convinced that Hendershott’s theoretical profits correlate to actual profits in reality: latency arbitrage alone, he says, is worth billions of dollars to high-frequency traders. What’s more, he says, those billions of dollars are “coming out of your retirement account”.
I have to say I’m weirdly impressed by Curran’s sophisticated argument for why these theoretical profits must be costing small investors billions of dollars a year: “every mickle”, we’re told, “makes a muckle”. This argument has the advantage of being unfalsifiable — but, sadly, it’s also complete nonsense. (I especially love the idea that mickles are more likely to become muckles “in a tough market”, whatever that’s supposed to mean.)
The fact is that “the little guy” has never had better execution than he has right now. To oversimplify wildly, let’s divide Wall Street into two groups: the sell side, the price-makers who provide liquidity, and the buy side, the price-takers, who simply decide whether to accept the market’s offer or not. If you’re looking at the current bid-offer spread on a stock (also known as NBBO, for national best bid/offer), then the bid is the best current price at which a sell-side firm will buy the stock from you, while the offer is the price you’ll have to pay to buy it. The difference between the two prices, these days, is lower than it has ever been, and small investors can normally buy or sell as much of any given stock as they like, right at NBBO, with execution in a fraction of a second. That wasn’t the case ten years ago.
Looked at through Curran’s eyes, the “little guy” is always a price taker. He doesn’t go out there into the market posting offers and waiting to see whether anybody will hit them; he just looks to see what offers there are, and if he likes the price being offered, he takes it. That kind of investor — and there are a lot of them out there — has never had it so good, precisely because there are so many HFT shops these days, competing to provide liquidity to the buy side and to receive the small sums of money that exchanges pay to the price-makers rather than the price-takers.
High frequency trading, along with its close relative decimalization, has been fantastic for price takers. They get better prices, they get them faster than ever, and the transaction costs associated with a “round trip” — buying a position and then selling it again — have never been lower. There’s some debate about whether it’s easier or harder than it used to be to trade in size; the jury’s still out on that one, but technology like dark pools has helped there, too. And if you’re big, then there are no shortage of VWAP algorithms and the like which you can use to try to beat the HFT bots at their own game.
Curran disagrees, and cites another paper — this one by Michael Wellman and Elaine Wah University of Michigan. (He didn’t link to this one, either, but a bit of googling found it here.) “Like others before them,” writes Curran, “Wellman and Wah’s study found latency arbitrage was eating investor profits.”
In fact, the Wellman-Wah paper finds no such thing: it’s not an empirical paper at all, and makes no attempt whatsoever to quantify investor profits, be they real or foregone. Instead, it’s an entirely theoretical thought experiment, where an “infinitely fast arbitrageur profits from market fragmentation” at the expense of “zero-intelligence trading agents”.
It’s easy to agree with Wellman and Wah that if there were a lot of risk-free latency arbitrage going on, then the victims would be “zero-intelligence trading agents”, or, as Larry Summers likes to call them, noise traders. But there’s a lot more to HFT than “rent space in a co-located server rack, find risk-free latency arbitrage opportunities, profit!” And while there are, still, idiots (look around), there are fewer of them than there used to be during the go-go day-trading days of the late 1990s. They learned their lesson during the dot-com bust, and with the rise of HFT there are very few small investors left who really believe they’re competing on a level playing field.
Note that it’s traders who lose money when HFT bots make money; if you’re a buy-and-hold investor, you really don’t care what’s going on behind the scenes at all. You just want the best execution for your orders — and right now, in general, execution for small investors is excellent. If you’re day-trading leveraged ETFs, on the other hand, then you’re basically just gambling: intraday moves are essentially random. Those people, over time, will end up losing money to the high-frequency traders.
Still, Wellman and Wah — and Curran — are concerned enough about the plight of the zero-intelligence trading agents that they propose a solution to this problem:
The authors suggest that the perpetual motion tape be replaced by a stop-motion tape. Instead of a continuous, free-for-all market, the session would take the form of a series of lightning-fast-auctions at intervals of a few milliseconds. This would give exchanges a reasonable amount of time to disseminate information (most only take a few thousandths of a second to catch up on the “direct access” feeds). It would also give traders a reasonable amount of time to place bids and offers on a given stock. The average investor would not see the difference because prices on active stocks would still be changing many times per second.
I’ve proposed something similar myself — last year, I said that a stock market where there was a mini-auction for every stock once per second would cause no measurable harm to investors, and would make the stock market as a whole less brittle. I’m no fan of HFT, and a discontinuous market would indeed put a stop to most of its excesses.
But what kind of a world do we live in when someone like Curran can claim with a straight face that “a few milliseconds” is “a reasonable amount of time to place bids and offers on a given stock”? Or where being able to see a stock price “changing many times per second” is considered an important feature of any stock market? The answer actually tells you a lot about the real financial victims of HFT. If you’re just a bystander, looking at stock prices changing many times per second, then you are not losing money to the algobots; in fact, you probably benefit from them, when you make a trade. If, on the other hand, you are not a high-frequency trader but you are the kind of person who thinks that “a few milliseconds” is “a reasonable amount of time to place bids and offers on a given stock”, well, then in that case you might indeed be a victim of the HFT crew: you’re trying to compete with them, and you’re probably losing.
The point here is that making improbable claims about the costs of HFT to small investors is not going to get you very far. The real costs of HFT are found in fat tails and systemic risks and the problems that are endemic to ultra-complex systems. It would certainly be rhetorically very neat and easy if we could plausibly declare that small investors are being hurt by high-frequency traders. But the truth is that they’re not: they’re actually being helped by HFT. It’s the market as a whole which is being put at risk by these algorithms, not the “little guy”. And while I’d welcome a move to a discontinuous market, I don’t for a minute think that such a move would save small investors any money at all, let alone billions of dollars.
*Update: Eric Hunsader has found a cached version of the paper, and — as I suspected — it doesn’t say anything like what Curran said it says. Hendershott did not apply any kind of trading strategy to Apple’s price history on May 9, and did not come up with $377,000 in theoretical profits by doing so. Here’s the relevant bit of the paper, which tries to recreate a “synthetic” NBBO and then compare it to the official SIP NBBO:
For 3.51 milliseconds of each second the SIP NBBO and synthetic NBBO differ. This could result in a buy or sell market order going to the wrong market roughly half that often: 0.175% of the time. Figure 5 shows that the average price dislocation is $0.034. Simply multiplying this times the percentage of the time a dislocation occurs yields an expected price dislocation of $0.006 per 100 shares for a market order entered randomly throughout the day. Multiplying this dollar amount by Apple’s May 9 trading volume of 17,167,989 shares yields $942, representing 0.001 of a basis point of dollar volume traded. This suggests that investors randomly routing market orders are unlikely to face meaningful costs due to data latency.
Yep, the “cost to the little guy” is not $377,000 per day; in fact, according to the paper, it’s just $0.006 per 100 shares, or one thousandth of a basis point. Which adds up to a whopping $942 per day. None of which can be captured by latency arbitrage. Hendershott’s conclusion is not that the little guy is losing out on billions: it’s that “investors randomly routing market orders are unlikely to face meaningful costs due to data latency”.
So where does the $377,000 figure come from? It comes from hypothetical latency traders trying to pick off other (equally hypothetical) active traders who do things like place orders in dark pools at the NBBO midpoint:
Assume BATS updates AAPL bid price from $530 to $531, and the ask price remains at $532. This changes the mid-price from $531 to $531.5. In the first 1.5 milliseconds, slower traders are not aware of the price change. If some such regular traders have placed an order to trade at mid-price in a dark pool, then a faster trader can buy the stock at $531 in dark pool when the synthetic NBBO gets updated. After 1.5 milliseconds, the trader can sell it for $531.5 in the dark pool. In this case the trade gains 50% of the price dislocation. Dark pools represent roughly 11% of trading volume, corresponding to 1,888,478 share of AAPL on May 9. If half of the average dislocation of 0.034 cents is captured on this volume then the fast trader would make a profit of $376,900 in a single stock on a single day. While Apple is one of the highest-volume stocks and this almost certainly represents an upper bound on the profits of strategies based on latency, the dollar figure illustrates the possible magnitude of profits and costs stemming from latency for traders continuously in the market.
Or, in English: if there are people trading continuously in the market who don’t have low-latency feeds, and those people are using the NBBO to determine their trading strategy, then those people can get picked off by hypothetical HFT bots. But clearly, those people are not “the little guy on Wall Street”, and no one in reality is making anything like $377,000 a day from HFT.
In fact, even the tiny $942 figure doesn’t represent HFT profits, it just represents potential losses for small investors. Curran has completely misrepresented Hendershott’s paper. His entire story, including the headline, is basically just false.
Update 2: The paper itself is still hosted on Henderson’s website here, he just doesn’t link to it from his list of publications.